In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[EX Q]: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. 
 Question: What is the source of the user interaction data? 
[EX A]: Sociability from ego-network on Twitter

[EX Q]: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?
[EX A]: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values

[EX Q]: In this study, we focused on the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research. we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter. We succeeded in discovering the relationship between LDA topics and paper features and also obtained the researchers' interest in research field. To perform approximate inference and learning LDA, there are many inference methods for LDA topic model such as Gibbs sampling, collapsed Variational Bayes, Expectation Maximization. Gibbs sampling is a popular technique because of its simplicity and low latency. However, for large numbers of topics, Gibbs sampling can become unwieldy. In this paper, we use Gibbs Sampling in our experiment in section 5. 
 Question: How they utilize LDA and Gibbs sampling to evaluate ISWC and WWW publications?
[EX A]:
the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter

